High performance constraint satisfaction problem solving: State-recomputation versus state-copying.

Constraint Satisfaction Problems (CSPs) in Artificial Intelligence have been an important focus of research and have been a useful model for various applications such as scheduling, image processing and machine vision. CSPs are mathematical problems that try to search values for variables according to constraints. There are many approaches for searching solutions of non-binary CSPs. Traditionally, most CSP methods rely on a single processor. With the increasing popularization of multiple processors, parallel search methods are becoming alternatives to speed up the search process. Parallel search is a subfield of artificial intelligence in which the constraint satisfaction problem is centralized whereas the search processes are distributed among the different processors. In this thesis we present a forward checking algorithm solving non-binary CSPs by distributing different branches to different processors via message passing interface and execute it on a high performance distributed system called SHARCNET. However, the problem is how to efficiently communicate the state of the search among processors. Two communication models, namely, state-recomputation and state-copying via message passing, are implemented and evaluated. This thesis investigates the behaviour of communication from one process to another. The experimental results demonstrate that the state-recomputation model with tighter constraints obtains a better performance than the state-copying model, but when constraints become looser, the state-copying model is a better choice.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .Y364. Source: Masters Abstracts International, Volume: 44-01, page: 0417. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Parallelism in Constraint Programming

Writing efficient parallel programs is the biggest challenge of the software industry for the foreseeable future. We are currently in a time when parallel computers are the norm, not the exception. Soon, parallel processors will be standard even in cell phones. Without drastic changes in hardware development, all software must be parallelized to its fullest extent. Parallelism can increase performance and reduce power consumption at the same time. Many programs will execute faster on a dual-core processor than a single core processor running at twice the speed. Halving the speed of a processor can reduce the power consumption up to four times. Hence, parallelism gives more performance per unit of power to efficient programs. In order to make use of parallel hardware, we need to overcome the difficulties of parallel programming. To many programmers, it is easier to learn a handful of small domain-specific programming languages than to learn efficient parallel programming. The frameworks for these languages can then automatically parallelize the program. Automatically parallelizing traditional programs is usually much more difficult. In this thesis, we study and present parallelism in constraint programming (CP). We have developed the first constraint framework that automatically parallelizes both the consistency and the search of the solving process. This allows programmers to avoid the difficult issues of parallel programming. We also study distributed CP with independent agents and propose solutions to this problem. Our results show that automatic parallelism in CP can provide very good performance. Our parallel consistency scales very well for problems with many large constraints. We also manage to combine parallel consistency and parallel search with a performance increase. The communication and load-balancing schemes we developed increase the scalability of parallel search. Our model for distributed CP is orders of magnitude faster than traditional approaches. As far as we know, it is the first to solve standard benchmark scheduling problems

Exploring Alternative Restoration Techniques in Constraint Programming

Master'sMASTER OF SCIENC

Programming constraint services

This thesis presents design, application, implementation, and evaluation of computation spaces as abstractions for programming constraint services at a high level. Spaces are seamlessly integrated into a concurrent programming language and make constraintbased computations compatible with concurrency through encapsulation. Spaces are applied to search and combinators as essential constraint services. State-of-the-art and new search engines such as visual interactive search and parallel search are covered. Search is expressive and concurrency-compatible by using copying rather than trailing. Search is space and time efficient by using recomputation. Composable combinators, also known as deep-guard combinators, stress the control facilities and concurrency integration of spaces. The implementation of spaces comes as an orthogonal extension to the implementation of the underlying programming language. The resulting implementation is shown to be competitive with existing constraint programming systems.Diese Dissertation beschreibt Entwurf, Verwendung, Implementierung und Evaluierung von Computation Spaces für die Programmierung von Constraintdiensten. Spaces werden in eine nebenläufige Programmiersprache integriert. Sie fungieren als Kapseln für Berechnungen mit Constraints. Dadurch wird die Kompatibilität zu nebenläufigen Berechnungen gewährleistet. Suche und Kombinatoren sind zentrale Constraintdienste, die mit Spaces programmiert werden. Es werden sowohl übliche, als auch vollkommen neue Suchmaschinen, wie zum Beispiel interaktive Suche und parallele Suche, vorgestellt. Durch Kopieren wird Suche ausdrucksstark und kompatibel mit Nebenläufigkeit. Durch Wiederberechnung wird Suche effizient hinsichtlich Speicherbedarf und Laufzeit. Kombinatoren, die ineinander geschachtelt werden können (so genannte deep-guard Kombinatoren), verdeutlichen die Kontrollmöglichkeiten von Spaces. Die Implementierung von Spaces erfolgt als orthogonale Erweiterung einer Implementierung für die zugrundeliegende Programmiersprache. Das Ergebnis ist konkurrenzfähig zu existierenden Constraintprogrammiersystemen

ACE: And/or-parallel copying-based execution of logic programs

In this paper we present a novel execution model for parallel implementation of logic programs which is capable of exploiting both independent and-parallelism and or-parallelism in an efficient way. This model extends the stack copying approach, which has been successfully applied in the Muse system to implement or-parallelism, by integrating it with proven techniques used to support independent and-parallelism. We show how all solutions to non-deterministic andparallel goals are found without repetitions. This is done through recomputation as in Prolog (and in various and-parallel systems, like &-Prolog and DDAS), i.e., solutions of and-parallel goals are not shared. We propose a scheme for the efficient management of the address space in a way that is compatible with the apparently incompatible requirements of both and- and or-parallelism. We also show how the full Prolog language, with all its extra-logical features, can be supported in our and-or parallel system so that its sequential semantics is preserved. The resulting system retains the advantages of both purely or-parallel systems as well as purely and-parallel systems. The stack copying scheme together with our proposed memory management scheme can also be used to implement models that combine dependent and-parallelism and or-parallelism, such as Andorra and Prometheus

And-or parallel prolog: a recomputation based approach

We argüe that in order to exploit both Independent And- and Or-parallelism in Prolog programs there is advantage in recomputing some of the independent goals, as opposed to all their solutions being reused. We present an abstract model, called the Composition-Tree, for representing and-or parallelism in Prolog Programs. The Composition-tree closely mirrors sequential Prolog execution by recomputing some independent goals rather than fully re-using them. We also outline two environment representation techniques for And-Or parallel execution of full Prolog based on the Composition-tree model abstraction. We argüe that these techniques have advantages over earlier proposals for exploiting and-or parallelism in Prolog

Logic programming in the context of multiparadigm programming: the Oz experience

Oz is a multiparadigm language that supports logic programming as one of its major paradigms. A multiparadigm language is designed to support different programming paradigms (logic, functional, constraint, object-oriented, sequential, concurrent, etc.) with equal ease. This article has two goals: to give a tutorial of logic programming in Oz and to show how logic programming fits naturally into the wider context of multiparadigm programming. Our experience shows that there are two classes of problems, which we call algorithmic and search problems, for which logic programming can help formulate practical solutions. Algorithmic problems have known efficient algorithms. Search problems do not have known efficient algorithms but can be solved with search. The Oz support for logic programming targets these two problem classes specifically, using the concepts needed for each. This is in contrast to the Prolog approach, which targets both classes with one set of concepts, which results in less than optimal support for each class. To explain the essential difference between algorithmic and search programs, we define the Oz execution model. This model subsumes both concurrent logic programming (committed-choice-style) and search-based logic programming (Prolog-style). Instead of Horn clause syntax, Oz has a simple, fully compositional, higher-order syntax that accommodates the abilities of the language. We conclude with lessons learned from this work, a brief history of Oz, and many entry points into the Oz literature.Comment: 48 pages, to appear in the journal "Theory and Practice of Logic Programming